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We propose a new fast algorithm to estimate any sparse generalized linearmodel with convex or non-convex separable penalties. Our algorithm is able tosolve problems with millions of samples and features in seconds, by relying oncoordinate descent, working sets and Anderson acceleration. It handlespreviously unaddressed models, and is extensively shown to improve state-of-artalgorithms. We provide a flexible, scikit-learn compatible package, whicheasily handles customized datafits and penalties.

Beyond L1: Faster and Better Sparse Models with skglm